Computational Polypharmacology Research Articles

Advances in Computational Polypharmacology.

In drug discovery, polypharmacology encompasses the use of small molecules with defined multi-target activity and in vivo effects resulting from multi-target engagement. Multi-target compounds are often efficacious in the treatment of complex diseases involving target and pathway networks, but might also elicit unwanted side effects. Computational approaches such as target prediction or multi-target ligand design have been used to support polypharmacological drug discovery. In addition to efforts directed at the identification or design of new multi-target compounds, other computational investigations have aimed to differentiate such compounds from potential false-positives or explore the molecular basis of multi-target activities. Herein, a concise overview of the field is provided and recent advances in computational polypharmacology through machine learning are discussed.

A “Receptophore Model” for Local Anesthetics Binding Site in Cardiac Ion Channels

Intoxication with local anesthetics may provoke arrhythmias by interaction with cardiac ion channels such as NaV1.5, KV1.5 and TASK-1. While NaV1.5, KV1.5 and TASK-1 channels have different sequences and structures, they share local similarities in the architecture of the binding site for local anesthetics. Using computational polypharmacology and multi-target/structure-based methods such as Geomfinder (https://jcheminf.biomedcentral.com/articles/10.1186/s13321-016-0131-9), PocketMatch (http://proline.physics.iisc.ernet.in/pocketmatch/), and MultiBind (http://bioinfo3d.cs.tau.ac.il/MultiBind/) we searched for local anesthetics Binding Site Similarities (BSS) between NaV1.5, KV1.5 and TASK-1 channels, converting 3D coordinates of proteins into easy patterns to compare.

Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration.

The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.

Generating Drug-Gene Association for Vibrio Cholerae using Ontological Profile Similarity

Objective: The measure of biomedical literature has been expanding quickly in the most recent decade. Text mining systems can analysis this vast scale information, shed light onto complex medication instruments and concentrate connection data that can bolster computational polypharmacology. The key idea of the paper is to find the drug candidate for the disease cholera, which is caused due to the organism Vibrio cholerae. Method: The technique estimates the Pointwise Mutual Information (PMI) among protein name obtained from the UniprotKB and the Medical Subject Headings that contain drug terms. Based on the PMI scores, gene/protein profiles and drug are produced and candidate drug-gene/protein associations are constructed when evaluating the relevancy of their profiles. Findings: The association between protein and drug is found and the drug candidates are proposed. Applications: The similar technique can be applied to find the drug candidate for various diseases which reduces the drug release cost in the market.

Enhancing the Enrichment of Pharmacophore-Based Target Prediction for the Polypharmacological Profiles of Drugs.

PharmMapper is a web server for drug target identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effect risk evaluation. But due to the inherent nondiscriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit score matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit score distribution of the background database, thus enabling estimation of the probability of finding a given target randomly with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed which confirmed that the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reaction related off-targets.

Computational polypharmacology comes of age.

In the last years, the “one target, one drug” paradigm that has traditionally dominated drug discovery has been deeply challenged by the evidence that small molecules interact simultaneously with multiple targets, a phenomenon known as polypharmacology. Today, polypharmacology is recognized as a new valuable opportunity for drug discovery and development. It is now well established that drug molecules typically bind to several targets, and that their efficacy and safety is mostly dependent on their polypharmacological profile (Jalencas and Mestres, 2012; Peters, 2013; Anighoro et al., 2014). Indeed, one of the most common reasons for terminating a drug discovery program has been promiscuity or lack of selectivity of the developed compounds. This leads to important considerations regarding the polypharmacology inherent in chemical structures and its possible exploitation for drug discovery. First, side effects caused by drug binding to unwanted off-targets (adverse polypharmacology) should be identified as early as possible in the drug discovery pipeline. Second, potential synergistic effects arising from hitting multiple targets (beneficial polypharmacology) should be taken into consideration and thoroughly incorporated in the drug design strategy. Third, polypharmacological approaches have the potential to redirect stalled drug discovery projects and to reposition valuable hits or leads (drug repositioning). Finally, prediction of polypharmacological profiles can be used to uncover new macromolecular targets for already known or new developing drugs (target identification and deconvolution). In all these areas, computational polypharmacology is gaining a foothold in drug discovery, as witnessed by the increasing number of publications reporting theoretical approaches and methods specifically put forward to address these needs.

Prediction of drug gene associations via ontological profile similarity with application to drug repositioning

The amount of biomedical literature has been increasing rapidly during the last decade. Text mining techniques can harness this large-scale data, shed light onto complex drug mechanisms, and extract relation information that can support computational polypharmacology. In this work, we introduce a fully corpus-based and unsupervised method which utilizes the MEDLINE indexed titles and abstracts to infer drug gene associations and assist drug repositioning. The method measures the Pointwise Mutual Information (PMI) between biomedical terms derived from the Gene Ontology and the Medical Subject Headings. Based on the PMI scores, drug and gene profiles are generated and candidate drug gene associations are inferred when computing the relatedness of their profiles. Results show that an Area Under the Curve (AUC) of up to 0.88 can be achieved. The method can successfully identify direct drug gene associations with high precision and prioritize them. Validation shows that the statistically derived profiles from literature perform as good as manually curated profiles. In addition, we examine the potential application of our approach towards drug repositioning. For all FDA approved drugs repositioned over the last 5years, we generate profiles from publications before 2009 and show that new indications rank high in the profiles. In summary, literature mined profiles can accurately predict drug gene associations and provide insights onto potential repositioning cases.

GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning

Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES "computational polypharmacology fingerprint" (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house "experimental polypharmacology fingerprint" (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates.

Computational Polypharmacology with Text Mining and Ontologies

Huge volumes of data, produced by microarrays and next- generation sequencing, are now at the fingertips of scientists and allow to expand the scope beyond conventional drug de- sign. New promiscuous drugs directed at multiple targets promise increased therapeutic efficacy for treatment of multi- factorial diseases. At the same time, more systematic tests for unwanted side effects are now possible. In this paper, we focus on the application of text mining and ontologies to support experimental drug discovery. Text mining is a high- throughput technique to extract information from millions of scientific documents and web pages. By exploiting the vast number of extracted facts as well as the indirect links between them, text mining and ontologies help to generate new hypotheses on drug target interactions. We review latest applications of text mining and ontologies suitable for target and drug-target interaction discovery in addition to conventional approaches. We conclude that mining the literature on drugs and proteins offers unique opportunities to support the laborious and expensive process of drug development.